MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Sensors

Mohd Khanapi Abd Ghani¹  Othman Mohd¹  AmmarAwad Mutlag¹  KarrarHameed Abdulkareem²  MazinAbed Mohammed³  Isabel de la Torre Díez⁴  SalamaA. Mostafa⁵  Gonçalo Marques⁶  MashaelS. Maashi⁷ 

[1] Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia;College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq;College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 55431, Anbar, Iraq;Department of Signal Theory and Communications, University of Valladolid, 47011 Valladolid, Spain;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor 86400, Malaysia;Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal;Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;

关键词: fog computing;    cloud computing;    healthcare;    multi-agent system;    critical tasks management;    scheduling optimization;   

DOI  :  10.3390/s20071853

来源: DOAJ

【 摘 要 】

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

【 授权许可】

Unknown